from transformers import AutoTokenizer, AutoModel
import torch
import torchvision.transforms as T
from PIL import Image

from torchvision.transforms.functional import InterpolationMode
import sys
import argparse

sys.path.append("/root/code/BeautyMaster/beautymaster")
from src.infer_vlm import infer_vlm_func
from src.infer_rag import infer_rag_func


IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)


def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose([
        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
        T.ToTensor(),
        T.Normalize(mean=MEAN, std=STD)
    ])
    return transform


def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float('inf')
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio


def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set(
        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
        i * j <= max_num and i * j >= min_num)
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size)

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images


def load_image(image_file, input_size=448, max_num=6):
    image = Image.open(image_file).convert('RGB')
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values


def main():

    path = "/root/model/InternVL-Chat-V1-5"
    # If you have an 80G A100 GPU, you can put the entire model on a single GPU.
    model = AutoModel.from_pretrained(
        path,
        torch_dtype=torch.bfloat16,
        low_cpu_mem_usage=True,
        trust_remote_code=True,
        load_in_8bit=False).eval().cuda()


    tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
    # set the max number of tiles in `max_num`

    weather = "10~15摄氏度"
    season = "春季"
    determine = "逛街"

    generation_config = dict(
        num_beams=1,
        max_new_tokens=512,
        do_sample=False,
    )
    image_counts=[]
    images = []
    source="/root/data/test_data/"
    get_num_list = [1,3,3,3]
    model_candidate_clothes_list = infer_rag_func(source, get_num_list) #for test, now get list randomly.
    print(model_candidate_clothes_list)
    for i in range(len(model_candidate_clothes_list)):
        pixel_values1 = load_image(model_candidate_clothes_list[i], max_num=10).to(torch.bfloat16).cuda()

        image_counts.append(pixel_values1.size(0))
        images.append(pixel_values1)
    pixel_values = torch.cat(images, dim=0)

    questions = "你是一个专业的服装搭配大师，现在需要你根据输入的图片，为客户设计一套穿搭，包括：衣服，裤子或者单选裙子，其中第{}张图片为客户的全身照，第{}张为候选的衣服，第{}张为候选的裤子，第{}张为候选的裙子，注意：当前处在{}， 气温{}， 客户去{}，请在候选的图片中为客户设计三个合适的穿搭方案，方案包括一件衣服和一条裤子或者单选一条裙子，并对每个方案打分，分值在0~100之间。如果是衣服和裤子的搭配，最后输出的格式如下：搭配方案号{}+候选衣服中的第{}件衣服+候选裤子中的第{}条裤子+打分+简明扼要的理由；如果是单裙子的搭配，最后输出的格式如下：搭配方案号{}+候选裙子中的第{}条裙子+打分+简明扼要的理由".format("1", "2~4", "5~7", "8~10", season, weather, determine, 'n', 'n', 'n', 'n', 'n', 'n')
    history = None
    responses, history = model.chat(tokenizer, pixel_values, questions, generation_config, history=history, return_history=True)
    print(questions, responses)
    # for question, response in zip(questions, responses):
      
if __name__ == "__main__":
    main()      
